Tasks:  We consider sinusoid regression and image classification tasks. For the regression problem, each task involves regressing from the input to the output of a sine wave. In particular, we train a meta model using 3 sinusoidal regression tasks with amplitudes 2, 6, and 10, respectively, and then fine tune it to any sinusoid wave with amplitude ranges from \(0.1\)  to 10 using \(K\)  samples. The loss is the mean-squared error between the prediction \(f(\mathbf {x})\)  and true value. The regressor we use is the same one used in [1]}, which is a neural network model with 2 hidden layers of size 40 with ReLU nonlinear activation functions. We choose \(K=40\)  for the sinusoid regression problem.
